A textbook teaching foundational ideas in reinforcement learning with examples in finance. Written by Ashwin Rao and Tikhon Jelvis.
Reinforcement Learning (RL) is emerging as a viable and powerful technique for solving a variety of complex business problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Although RL is classified as a branch of Machine Learning (ML), it tends to be viewed and treated quite differently from other branches of ML (Supervised and Unsupervised Learning). Indeed, RL seems to hold the key to unlocking the promise of AI – machines that adapt their decisions to vagaries in observed information, while continuously steering towards the optimal outcome. It’s penetration in high-profile problems like self-driving cars, robotics and strategy games points to a future where RL algorithms will have decisioning abilities far superior to humans.
The first edition of the book is now available from Routledge. You can:
You can also download errata for the print version.
The rl directory in this repo contains the code used in this book, with a simple framework for reinforcement learning as well as fleshed out examples for each chapter.
The Python code for the book requires a few additional libraries. We can manage our Python dependencies with a venv.
First, create a venv:
> nix-shell
[nix-shell:~/Documents/RL-book]$ python -m venv .venvThen, each time you're working on this project, make sure to activate the venv:
> source .venv/bin/activateOnce the venv is activated, you should see a (.venv) in your shell prompt:
(.venv) RL-book:RL-book>Now you can use pip to install the needed dependencies inside the venv:
(.venv) RL-book:RL-book>pip install -r requirements.txtIf you want additional libraries, you can install them explicitly:
(.venv) RL-book:RL-book> pip install matplotlib